In critical storage systems, it is desirable to estimate the amount of electricity consumed by the system or its individual components so that adequate power resources can be provided. Some component manufacturers publish only the highest power consumption estimates for certain system components and do not provide online power metering information. Such estimates, however, are not always accurate and do not particularly distinguish between power consumption during different activity levels (e.g., periods when a component is running in an idle mode or various storage access pattern or levels).
In order to get accurate power usage information for different activity levels for a component, it is possible to connect several digital multimeters to the component (e.g., a disk drive, a flash drive, etc.). The number of multimeters may depend on the various input voltages and the metering technology. A large storage array containing several hundred Terra Bytes of storage space, for example, will need hundreds or thousands of multimeters and a complex collection and processing system to calculate current and voltage information.
Such architecture is somewhat impractical, however, because the addition of a large number of multimeters to a storage system interferes and affects the total power consumption and the physical space within the system. Such implementation may also require substantial measurement processing at the management layer. Further, the decision whether to install the metering equipment depends on the cost of the metering equipment installation versus the benefit that can be achieved by the resulting measurement.
Unfortunately, cost-benefit analysis for deploying the above architecture in most systems is unknown. On the other hand, there is no doubt that more accurate and workload dependant power measurements can help in making a better decision on how to manage power consumption and the availability of power resources that are critical to maintaining a storage system viable at all times.
In addition, hardware equipment by itself cannot sufficiently determine consumed power by a logical volume, particularly in virtualized data center applications that store information by distributing the logical volume data over multiple disks and multiple fragments of disks.
Moreover, offline tools such as capacity planning tools and also online tools that predict storage capacity, performance, and consumed power given configuration and host-level input/output (I/O) pattern must use offline power measurement and estimates in order to predict activity-dependant power estimations.